From e48381af154b9b353bb329986a2c9b66ed24bccf Mon Sep 17 00:00:00 2001 From: Yuduo Wu Date: Tue, 27 Nov 2018 17:17:51 -0800 Subject: [PATCH] update awesome ai infrastructures --- README.md | 8 ++++---- images/nvidia-rapids-arch.png | Bin 20531 -> 48424 bytes 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 9073d99..88dd242 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ :tent: Awesome AI Infrastructures :tent: -

+

:orange_book: List of AI infrastructures (a.k.a., machine learning systems, pipelines, and platforms) for machine/deep learning training and/or inference in production :electric_plug:. Feel free to contribute / star / fork / pull request. Any recommendations and suggestions are welcome :tada:.

@@ -44,7 +44,7 @@ in **no specific order**. This list cares more about overall architectures of AI ### [KubeFlow](https://www.kubeflow.org/) - The Machine Learning Toolkit for [Kubernetes](https://kubernetes.io/) ([Google](https://www.google.com/about/)) -> The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on **[Kubernetes](https://kubernetes.io/)** simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running [Kubernetes](https://kubernetes.io/), you should be able to run Kubeflow. +> The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on [Kubernetes](https://kubernetes.io/) simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running [Kubernetes](https://kubernetes.io/), you should be able to run Kubeflow. > Kubeflow started as an open sourcing of the way Google ran [TensorFlow](https://www.tensorflow.org/) internally, based on a pipeline called TensorFlow Extended. @@ -87,9 +87,9 @@ in **no specific order**. This list cares more about overall architectures of AI > The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. -> RAPIDS is the result of contributions from the machine learning community and [GPU Open Analytics Initiative (GOAI)](http://gpuopenanalytics.com/) partners, such as [Anaconda](https://www.anaconda.com/), [BlazingDB](https://blazingdb.com/), [Gunrock](https://github.com/gunrock/gunrock), etc. +> RAPIDS is the result of contributions from the machine learning community and [GPU Open Analytics Initiative (GOAI)](http://gpuopenanalytics.com/) partners, such as [Anaconda](https://www.anaconda.com/), [BlazingDB](https://blazingdb.com/), [__Gunrock__](https://github.com/gunrock/gunrock), etc. -| [__blog__](https://rapids.ai/) | [__github__](https://github.com/RAPIDSai) | +| [__homepage__](https://rapids.ai/) | [__blog__](https://medium.com/rapids-ai) | [__github__](https://github.com/RAPIDSai) | #### Architecture: diff --git a/images/nvidia-rapids-arch.png b/images/nvidia-rapids-arch.png index c0cc26e5907bf14d4a37432e575593df92920cb2..20374ff48a7ab7c0a5bef0191f142aadc6f1c245 100644 GIT binary patch literal 48424 zcmeFXWpG?gvNkAYX31h%%*iH* z`{(}I*sr6a<8<~(s60<*Rd+pM%8F9R2m}aVU|`5H(&DOMV32NLVBi{XFdve91YZF# zFgy+`F)?KsF)>nQX9sgDTQe{)?chXDSamFgI=4wlH=U#mmkHj$oeUSrufx0+>Uq+t zP~Z|G(AZGeNT1P_(Z#XQ)w{n-D}OG{`9V(lj&|dFexG^0;qsi3<=NQ!@han+LjkLB zF1Wb(3WW_umOCB000YvGR-DjZqWunODT0a>z%oovr$0I~gVnt|`Rt!Gj}B(nl^x+Q z`gDHx?vQ(T7j^{!RvT1P?4FW-+Ji)*iYxa(;OO%1?H&$$akW@^EM350>X9r4vX=xbWu~d^h~^!{jaU zH=@=L|AO4xyNsq&94zzNDC4zUU~bkP4ogwMWdztc(VLyXHi?O4)8P9oy)0}a{#P(C z(F>}>wC)t-0fF4#dqv&=*9|aV3cTJ%sU;ZkPs?X_5*|_^I_JYueb3^D%P+qRhB9j! zJ)OV(IH|ug&APm2eWIvioZeV4)>7*GYWux|H!_TcI0B)Tb$L5g>1L7jy%!VTq~jO+ ziOc7UsJW)~UD2yx@z+#b)qdgbAs^Iq$>$Y<4=1gjv2Fm9A5J++3HgU4f*rnqTetmS z`D^%wm01!UJmOz=9fSU40!z*@B+F-*w$%#z0hc1`730~Ra?_60rxUo)J^74w?yh3f z&~cHB?ye$XU}`y#0C3#UfO2X$$NUu(?J-x^l(P^C#R9>7URJEb0HdyR`JvHU>a z#V_C`g-DMagikXyNzf2yx!+aYv$wk4i#gqjrO;9WoZwi%e_x!ZWE$rBOVz=i8_Rb4 zN1Z_*6Cf>~HooUMsl4SH->|geiA4%Y;`RSfl`74X-z=umH&d3S*%kJB?TyJ2Ma?aq z>OFwJp}+rLKlDD8ftV5iuPyc3vF!Qg?M4HxT_zy=sSsuqXqJzs%pgKY2^&)oJo9}p z30V5Rz`1uG`ChY|#gKu85@6D2wH5TH2AKz^@>Z2#C-=I7)8^&*){r!@k@TJcoaj2) zNA6O1i^oh2ZVx+=SQon^TZv`-j_3)y9DX9@X1m>0bLmIT^%iN~k*|ctK>ep{fZ{Z& z#jb&-bX3(v^^><9DTrFg%e&?vD41Ft=HXGw#p-uk18TBLNpcWga%2KWdxZV@Yn$FE z_i?5m(KnAL)Nu=(@4k*>+c59B&{<$Pm9Ss~8UB_KOkgY#;Eu1UTJokROvnwdB9H<( zL0Smxf4<){k$Fh6iGY_tDCEFK20&LsD|RC{)(I09$C9EDf|ZhD?)=!!!OMWL`0eQk zuMf>Ff@leQ-wkl=QcS|z!P|Pi$I2pb)yeja9RBedB z`G)|hFb!6C0E)<=GLt%7W(Zxb?g1YFu@&}KRBVdg5+g0J^Gg?+G!(cf@T(LY>1T9V zIL09O9-JDIO<<_VXL2^QL9}8tjZpef^FF~olRlPj5H;VmMIk=(#(@t{&8fB3ELr z&V9sdjnNSF6jdZrmn$(%XGu{{0YW7LPKYPxlus)h5PO{Zh*W)J!Gnn;l|(o6R!k9* z$Dl%03$W^5?ME^$Vx`w+NW!NN*XY&#Dzjv1OVxdpj?Hl_g{v|tQbX>M-#7q=hY;DjL)vnrz z1RY@_SRx8`*b$lr{ybxuN*T7buCS)W+U*%#P_OkRHXjT8*;^d|Jqb#si}M5IRO#PEi;d~XSUWJC;? 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